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Azure and AI that keep working after your team takes over.

Independent Azure consulting and AI consultancy for regulated enterprises in DACH and Benelux. Most projects start with one problem: an Azure estate that grew faster than its controls, or an AI pilot that never reached production. We fix that one first. The platform and the AI on it come from the same team, so nothing falls between two suppliers.

Azure platform and data

Your Azure estate grew faster than the controls around it. These are the parts we take on, each with what it has delivered before.

Azure migration and modernisation

Even when every server is ready to move, one undocumented link between two systems can hold up the whole move. It surfaces late, after the switch-over date is booked.

  • Migration at scale: we have moved 1,000+ servers and 300+ applications to Azure across multiple datacentres.
  • A recovery path when a move is already at risk. We took a national airline’s stalled datacentre-to-cloud migration onto a site-reliability-engineering (SRE) model and brought it back on track.
  • Dependency mapping and a cutover sequence agreed before anything moves.

Azure landing zone, governance, and hybrid networking

Set up your Azure foundation by hand and it drifts within weeks. New projects get spun up outside the rules, and you find the gaps when an auditor does, not before.

  • A hub-and-spoke landing zone with the infrastructure defined as code in Terraform, 85% of it on the last build.
  • Governance measured against the CIS security benchmark and held there by the policy set. We have taken subscriptions to 94%.
  • ExpressRoute and Azure Virtual WAN designs that keep latency under 10ms over hybrid links.
  • The network design lives in the same Terraform landing zone, so routing changes ship through the same policy pipeline as the rest of the platform.

Cloud security and identity

Security that lives outside the architecture is a checklist. A flat network lets a single compromised device on the operational-technology (OT) network reach the domain controller in one hop. Built inside the architecture, the control is enforced.

  • Zero Trust and PKI across both IT and OT, which saved one client €750k a year.
  • Entra ID and Key Vault designs built and run by engineers holding AZ-500, Microsoft’s Azure security certification.
  • A security review that maps each finding to the named control it answers to.

FinOps and Azure cost optimisation

Azure bills climb without anyone signing off on the increase: machines sized too big, capacity booked and never used. By the time finance asks, the cost is built into the architecture, and no setting will bring it down.

  • Rightsizing and reserved capacity to bring the bill down. On one estate that took 34% off the three-year total cost of ownership. What yours holds depends on where the spend sits.
  • On the same work we took 22% off live spend and secured a 28% Microsoft EA discount through Reserved Instances and Hybrid Benefit.
  • A FinOps practice with automated cost monitoring, not a one-off cleanup.

Power BI and Microsoft Fabric

Two dashboards disagree because the same number is defined twice, in two places, and neither is the agreed source of truth.

  • Enterprise Microsoft Fabric and Power BI architectures with governed semantic models.
  • Data integration on Azure SQL and Data Factory, built to a single source of truth.
  • A platform review that fixes the model, not just the visuals.

AI in production

One AI use case that pays back, built on the data you already hold. You do not need an Azure project with us first.

Use-case discovery and feasibility

Ideas are easy to list. The hard part is spotting the two or three that will actually pay back once they meet your data and your budget. A use case that looks clean on a whiteboard often turns on an SAP master-data change nobody scoped.

  • A shortlist of use cases ranked by feasibility and payback, scored against the data and systems you actually run.
  • Feasibility judged by people who have taken six-figure Azure AI projects through presales in manufacturing and chemicals.

Generative AI on Azure: document processing, search, and Copilot Studio

A search demo over ten documents impresses a room. Point the same setup at 500 users and a real document store, and it starts returning answers that are wrong, or that someone was never allowed to see.

  • Document intelligence that turns purchase-order PDFs into validated SAP orders, not just extracted text.
  • We have run enterprise search and RAG (retrieval that answers from your own documents) for 500-plus users, grounded and access-scoped to who is allowed to see what.
  • Copilot Studio and Azure AI Foundry implementations built on your data with the retrieval evaluated, not assumed.

Agentic systems and multi-agent orchestration

One agent calling one tool is easy. The moment several agents work together, they have to hold state and fail safely when something goes wrong, and most teams do not design for that before they build.

  • Agentic systems on the Microsoft Agent Framework with MCP (the standard that lets an agent call your tools) and A2A (agents calling each other), including custom MCP servers for your own tools and data.
  • Multi-agent orchestration across Claude, GPT, and Gemini, routed to the model that fits each step rather than one vendor by default.
  • Guardrails, state, and failure handling designed in, so an agent that goes wrong stops rather than improvises.

Machine learning and predictive analytics

A model that scores well in a notebook has proved nothing in production. What counts is the downtime it prevents or the price it gets right, on data that keeps changing.

  • Industrial computer vision and anomaly detection running against live process data.
  • Predictive models for downtime and pricing, built and validated on your historical data.
  • Every prediction ships with the validation record attached, so a downtime call or a price recommendation can be traced back to the data it was trained on.

AI governance and EU AI Act readiness

EU AI Act obligations attach to the system before it ships, not after. Retrofitting Annex IV documentation and a risk classification onto a system already in production costs far more than designing them in.

  • Each AI system mapped to its EU AI Act risk category before design, with the obligations that category carries built into delivery.
  • Annex IV technical documentation produced as an engineering deliverable, not a policy template handed over at the end.
  • An AI governance practice shaped by a practitioner who has built and led an AI Centre of Excellence.
  • The same practitioner holds the AB-731 AI Transformation Leader certification, carried into every project rather than handed to a subcontractor.

Running it after handover

The first serious incident tests the platform. Changing data tests the model. We design for both before go-live, and can stay on to run them.

Platform reliability and managed services

The first serious incident finds out whether anyone designed the recovery. With no runbook and no owner, the uptime target on paper does not hold.

  • An SRE operating model for the workloads that need it. It has held a 99.95% uptime SLA and cut mean time to recovery by 65%.
  • Up to 99.999% uptime achieved where the workload justified the design.
  • A retainer after handover, so the platform and the models on it stay governed.

MLOps, AIOps, and operations

The cost of an AI system shows up after launch. A model drifts as the data changes, and with no one watching, the drift goes unnoticed until a prediction is wrong at the worst moment.

  • AIOps with predictive analytics and anomaly detection watching the systems that watch your estate.
  • A path from proof of concept to production, with monitoring, retraining, and rollback defined before go-live.

Governance & compliance

  • Where residency is required, your platform runs in the Germany West Central Azure region. Models run in your tenant and your region.
  • GDPR Article 44 data-transfer restrictions addressed in the architecture, not a policy note.
  • Subscriptions measured against the CIS benchmark, with the policies enforced in Terraform. We have taken them to 94%.
  • Each AI system classified to its EU AI Act risk category before design begins. Annex IV technical documentation is written as an engineering deliverable.
  • Post-market monitoring designed in, not promised for later.
  • A documented data processing agreement and sub-processor chain.

Includes EU AI Act positioning and how we classify AI systems.

Read our governance approach

Where we sit

Between a freelancer and a large integrator.

A complex Azure and AI project usually narrows to two options. A freelancer gives you real depth, and no cover on the day one person is unavailable. A large integrator gives you a recognised brand and delivery cycles measured in quarters. Pavicore holds the middle: one accountable team from the first call to production, with the governance and continuity a freelancer cannot carry.

A freelancer

  • Real technical depth, and flexible to work with.
  • No governance capability and no continuity plan.
  • One person carries the project. If they drop out, it stops.

A large integrator

  • Knows the Microsoft platform, but AI often means Copilot licences and Azure credits.
  • Delivery runs through layers of project management. Every scope change becomes a change request before anyone writes code.
  • Day-rate contracts and cycles measured in quarters.

Pavicore

  • One accountable team from the first call to production. What we scope is what gets delivered, with no reset once delivery starts.
  • Architecture first: Azure and AI as one system, with GDPR Article 44 transfers and EU AI Act classification designed in.
  • The first project is scoped and priced before it starts. Delivery runs in weeks, not quarters.

Bring the problem. We scope the first project with you.

A short working session. You leave knowing what to fix first and which obligations apply to it, from GDPR Article 44 to the EU AI Act risk class.